IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1111.5397.html
   My bibliography  Save this paper

A Mathematical Method for Deriving the Relative Effect of Serviceability on Default Risk

Author

Listed:
  • Graham Andersen
  • David Chisholm

Abstract

The writers propose a mathematical Method for deriving risk weights which describe how a borrower's income, relative to their debt service obligations (serviceability) affects the probability of default of the loan. The Method considers the borrower's income not simply as a known quantity at the time the loan is made, but as an uncertain quantity following a statistical distribution at some later point in the life of the loan. This allows a probability to be associated with an income level leading to default, so that the relative risk associated with different serviceability levels can be quantified. In a sense, the Method can be thought of as an extension of the Merton Model to quantities that fail to satisfy Merton's 'critical' assumptions relating to the efficient markets hypothesis. A set of numerical examples of risk weights derived using the Method suggest that serviceability may be under-represented as a risk factor in many mortgage credit risk models.

Suggested Citation

  • Graham Andersen & David Chisholm, 2011. "A Mathematical Method for Deriving the Relative Effect of Serviceability on Default Risk," Papers 1111.5397, arXiv.org.
    • Handle: RePEc:arx:papers:1111.5397
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1111.5397
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    2. Wilson Sy, 2007. "A Causal Framework for Credit Default Theory," Research Paper Series 204, Quantitative Finance Research Centre, University of Technology, Sydney.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David E Allen & Robert Powell, 2012. "The fluctuating default risk of Australian banks," Australian Journal of Management, Australian School of Business, vol. 37(2), pages 297-325, August.
    2. Gady Jacoby & Chuan Liao & Jonathan A. Batten, 2007. "A Pure Test for the Elasticity of Yield Spreads," The Institute for International Integration Studies Discussion Paper Series iiisdp195, IIIS.
    3. ilya, gikhman, 2006. "Fixed-income instrument pricing," MPRA Paper 1449, University Library of Munich, Germany.
    4. Gordian Rättich & Kim Clark & Evi Hartmann, 2011. "Performance measurement and antecedents of early internationalizing firms: A systematic assessment," Working Papers 0031, College of Business, University of Texas at San Antonio.
    5. Gerardo Manzo & Antonio Picca, 2020. "The Impact of Sovereign Shocks," Management Science, INFORMS, vol. 66(7), pages 3113-3132, July.
    6. Christophe Hurlin & Jérémy Leymarie & Antoine Patin, 2018. "Loss functions for LGD model comparison," Working Papers halshs-01516147, HAL.
    7. Neus, Werner, 2014. "Eigenkapitalnormen, Boni und Risikoanreize in Banken," Die Unternehmung - Swiss Journal of Business Research and Practice, Nomos Verlagsgesellschaft mbH & Co. KG, vol. 68(2), pages 92-107.
    8. Giordani, Paolo & Jacobson, Tor & Schedvin, Erik von & Villani, Mattias, 2014. "Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(4), pages 1071-1099, August.
    9. Ulrike Malmendier & Vincenzo Pezone & Hui Zheng, 2023. "Managerial Duties and Managerial Biases," Management Science, INFORMS, vol. 69(6), pages 3174-3201, June.
    10. Lily Y. Liu, 2017. "Estimating Loss Given Default from CDS under Weak Identification," Supervisory Research and Analysis Working Papers RPA 17-1, Federal Reserve Bank of Boston.
    11. Kern, Markus & Rudolph, Bernd, 2001. "Comparative analysis of alternative credit risk models: An application on German middle market loan portfolios," CFS Working Paper Series 2001/03, Center for Financial Studies (CFS).
    12. Jeremy Leake, 2003. "Credit spreads on sterling corporate bonds and the term structure of UK interest rates," Bank of England working papers 202, Bank of England.
    13. Xin Huang & Hao Zhou & Haibin Zhu, 2012. "Systemic Risk Contributions," Journal of Financial Services Research, Springer;Western Finance Association, vol. 42(1), pages 55-83, October.
    14. Milne, Alistair, 2014. "Distance to default and the financial crisis," Journal of Financial Stability, Elsevier, vol. 12(C), pages 26-36.
    15. Boulanouar, Zakaria & Alqahtani, Faisal & Hamdi, Besma, 2021. "Bank ownership, institutional quality and financial stability: evidence from the GCC region," Pacific-Basin Finance Journal, Elsevier, vol. 66(C).
    16. Richardson, Grant & Taylor, Grantley & Lanis, Roman, 2015. "The impact of financial distress on corporate tax avoidance spanning the global financial crisis: Evidence from Australia," Economic Modelling, Elsevier, vol. 44(C), pages 44-53.
    17. Zhijian (James) Huang & Yuchen Luo, 2016. "Revisiting Structural Modeling of Credit Risk—Evidence from the Credit Default Swap (CDS) Market," JRFM, MDPI, vol. 9(2), pages 1-20, May.
    18. Masahiko Egami & Rusudan Kevkhishvili, 2020. "Time reversal and last passage time of diffusions with applications to credit risk management," Finance and Stochastics, Springer, vol. 24(3), pages 795-825, July.
    19. Pesaran, M. Hashem & Schuermann, Til & Treutler, Bjorn-Jakob & Weiner, Scott M., 2006. "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1211-1261, August.
    20. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1111.5397. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.